Comparative Assessment of Cognitive Function and Mood Dynamics in Patients with Depression and Eating Disorders in the Process of Treatment
Bibliographic record
Abstract
Eating disorders of bulimic type are among the most common comorbidities with depression. The objective is to evaluate cognitive function and mood dynamics in patients with depression and eating disorders in pharmacotherapy. In total, 52 outpatients, who met criteria for “major depressive episode” (ICD-10), participated. The level of depression was estimated with Hamilton Depression rating scale (HAM-D) and cognitive function–Montreal Cognitive Assessment (MoCa). Sample was divided into two groups. Patients of group 1 also met criteria for eating disorder of bulimic type and patients of group 2 did not have any eating disorder. Treatment included standard doses of SSRI. Assessments were performed after 2, 4 and 8 weeks (D14, D30, D60). The level of HAM-D was significantly greater (P < 0.05) in eating disorders group (16.75 ± 2.83 in group 1; 13.04 ± 1.93 in group 2 at screening) and significance was preserved till D60 (9.39 ± 2.54 in group 1; 6.32 ± 1.27 in group 2 at D60). Clinically significant antidepressive effect was revealed faster in group 2 (at D7) compared to group 1 (at D14). Overall score of MoCA was significantly lower (P < 0.05) in eating disorders group (20.33 ± 0.54 in group 1; 23.43 ± 2.32 in group 2 at screening) at all stages of treatment (23.39 ± 0.78 in group 1; 26.96 ± 3.27 in group 2 at D60) and it reached normal range (25 and more) only in group 2 at D60. Significant change from screening was revealed at D30 at group 2 and at D60 at group 1. Conclusion Eating disorder have an impact on SSRI treatment efficacy including antidepressive and procognitive effects. It is necessary to reveal eating disorders as a co-morbidity in patients with depression. Disclosure of interest The authors have not supplied their declaration of competing interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".